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              <text>A Novel Approach for Detection and Recognition of Traffic Signs for Automatic Driver Assistance System Under Cluttered Background</text>
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              <text>Driver assistance systems; SVM (Support Vector Machine) classifiers; YCbCr</text>
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              <text>Traffic sign detection and recognition is a core phase of Driver Assistance and Monitoring System. This paper focuses on the development of an intelligent driver assistance system there by achieving road safty. In this paper a novel system is proposed to detect and classify traffic signs such as warning and compulsory signs even for occluded and angular tilt images using Support Vector Machines. Exhaustive experiments are performed in order to demonstrate the efficiency of proposed method.  2019, Springer Nature Singapore Pte Ltd.</text>
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              <text>Manjunatha H.T.; Danti A.; ArunKumar K.L.</text>
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              <text>Communications in Computer and Information Science, Vol-1035, pp. 407-419.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-13-9181-1_36" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-13-9181-1_36&lt;/a&gt;
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              <text>ISSN: 18650929; ISBN: 978-981139180-4</text>
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              <text>Manjunatha H.T., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India; Danti A., Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; ArunKumar K.L., Department of MCA, Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India</text>
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